Active noise cancellation using recurrent radial basis function neural networks

Active noise cancellation using neural networks is addressed, with the aim being to derive an architecture/algorithm combination which provides spatiotemporal properties for faster convergence while maintaining a nonlinear dynamics approximation capability. Radial basis function neural networks, with feedback loops connecting the output and input of hidden neurons, are employed. A new learning algorithm suited for active noise cancellation, which is referred to as FX-LRRBF, is proposed. The structure/algorithm is implemented in real-time on a floating point DSP and experimentally carried-out to model the secondary path, which is required for attenuating acoustic noise.